Titlebook: Evolutionary Algorithms for Solving Multi-Objective Problems; Carlos A. Coello Coello,Gary B. Lamont,David A. Va Textbook 2007Latest editi

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Basic Concepts,ally, the complexity of some multiobjective optimization problems (MOPs) (e.g., very large search spaces, uncertainty, noise, disjoint Pareto curves, etc.) may prevent use (or application) of traditional OR MOPsolution techniques.

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Alternative Metaheuristics,roblems (e.g., the quadratic assignment problem) are known to be .-complete, they present real challenges to researchers. Additionally, many real-world problems (e.g., scheduling) require efficient approaches that can at least approximate . and . in a reasonable amount of time.

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Carlos A. Coello Coello,Gary B. Lamont,David A. VaDesigned for courses on Evolutionary Multi-objective Optimization and Evolutionary Algorithms.2nd Edition is completely updated and presents the latest research.Provides a complete set of teaching tut

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Genetic and Evolutionary Computationhttp://image.papertrans.cn/e/image/317814.jpg

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